17 citations · 46 across the 18 of their papers we have counts for
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Dynamic Jointly Batch Selection for Data Efficient Machine Translation Fine-Tuning
Mohammad Amin Ghanizadeh, Mohammad Javad Dousti
Data quality and its effective selection are fundamental to improving the performance of machine translation models, serving as cornerstones for achieving robust and reliable trans…
DeepQuestion: Systematic Generation of Real-World Challenges for Evaluating LLMs Performance
Ali Khoramfar, Ali Ramezani, Mohammad Mahdi Mohajeri +3
While Large Language Models (LLMs) achieve near-human performance on standard benchmarks, their capabilities often fail to generalize to complex, real-world problems. To bridge thi…
: Data-Driven LoRA Initialization for Low Resource Tasks
Javad SeraJ, Mohammad Mahdi Mohajeri, Mohammad Javad Dousti
Tuning large language models is essential for optimizing their performance across diverse applications, particularly in scenarios with limited data availability. Tuning large langu…
Towards Data-Efficient Language Models: A Child-Inspired Approach to Language Learning
Mohammad Amin Ghanizadeh, Mohammad Javad Dousti
In this work, we explain our approach employed in the BabyLM Challenge, which uses various methods of training language models (LMs) with significantly less data compared to tradit…